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The V-Ray Denoiser detects areas where noise is present in the rendered beauty image and render elements and smoothing them out.

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For animations, it is recommended to use the standalone denoiser tool. It performs frame blending and reduces flickering.

The V-Ray Denoiser performs an additional operation to the rendering and changing the denoising settings and denoising the image again does not require re-rendering the scene.

When rendering, the V-Ray Denoiser automatically adds a few render channels in the V-Ray Frame Buffer which are required to guide the denoising algorithm. The two denoising engines require different render elements. Some of them are standard render channels like the diffuse filter color, the reflection filter color etc. A few special channels are also generated for the Default V-Ray denoiser:

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Currently the scene can contain only one Denoiser Render Element. Future versions of V-Ray will support multiple Denoiser Render Elements with different settings.

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VRayDenoiser can be applied to the Viewport IPR by enabling the Use Denoiser option from V-Ray Renderer > Export > IPR.

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||out Network|| > V-Ray Render Elements node > V-Ray > Render Channel > Bump Normals


Denoising Engines

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VRayDenoiser offers a choice between the Default V-Ray denoiser and the NVIDIA AI denoiser. Each offers a different denoising algorithm that comes with different benefits. See the denoising engine examples below.

 

Default V-Ray denoiser - V-Ray's denoising algorithm. It can utilize the CPU or the GPU (AMD or NVIDIA GPUs) to perform the denoising. It is consistent when denoising render elements, as it applies the same denoising operator to all render channels, which means that it is recommended for denoising the render elements to be used for compositing back the beauty image. In addition, it comes with a standalone version, which is recommended for denoising animation by using frame blending.

NVIDIA AI denoiser - V-Ray's integration of NVIDIA's  AI-based denoising algorithm. The NVIDIA AI denoiser requires an NVIDIA GPU to work, regardless of whether the actual rendering was performed on the CPU or GPU. This means that rendering on the CPU will still require an NVIDIA GPU for denoising with the NVIDIA AI denoiser and has some advantages and drawbacks compared to the Default V-Ray Denoiser. For example, the NVIDIA AI denoiser performs the denoising faster, but is not consistent when denoising render elements. This means that there will be differences between the original RGB image and the one reconstructed from render elements that are denoised with the NVIDIA AI denoiser. It also doesn't support cross-frame denoising and will likely produce flickering when used in animation.

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The Nvidia AI denoiser only works on Nvidia GPUs starting with the Kepler GPU architecture (GTX 6xx series and newer). 


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Use GPU – Enables the usage of GPU if an OpenCL videocard is found.

Name The text added to the end of the rendered file, when saved as a separate file (e.g. myrender.Denoiser.vrimg).

Engine – Allows choosing between the Default V-Ray denoiser and the NVIDIA AI denoiserSee the denoising engine  examples below.

Preset – When using the Default V-Ray denoiser, the presets can be used to automatically set the Strength and Radius values.

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Type – Specifies which channels to denoise.

RGB Only
All Channels

Radius – Specifies the area around each pixel to be sampled for determining how to denoise a given pixel. Larger values produce smoother results, but slow down the denoiser.

Strength – Determines how strong the denoising operation will be. Larger values remove noise more aggressively, but may blur the image too much.

Generate Render Elements – Adds specific render elements that help the denoiser be more effective.

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Example: Default V-Ray denoiser


The example below illustrates how the Default V-Ray denoiser works after more samples are made with the Progressive image sampler. When the samples are too few, the're not enough information for the denoising to produce a smooth result.

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  Sampling pass 64

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The original vs. the denoised image after 64 passes.

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Example: NVIDIA AI denoiser

 

The example below illustrates how the NVIDIA AI denoiser works after more samples are made with the Progressive image sampler. When the samples are too few, there's not enough information for the denoising to produce a smooth result.

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The original vs. the denoised image after 64 passes.

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